A method, performed by a computing device, of measuring a volume of a polycystic kidney, the method comprising: acquiring a plurality of captured images of the polycystic kidney with a variety of shapes that are anatomically inconsistent, and photographing metadata and preprocessing the plurality of images to acquire a plurality of image patches (501a, 501b) of a specified size; inputting the plurality of image patches (501a, 501b) into a 3D CNN, Convolutional Neural Network, based neural network model and estimating a polycystic kidney region (502a, 502b) corresponding to each of the plurality of image patches (501a, 501b); measuring a volume of the polycystic kidney by using an area of the estimated polycystic kidney region (502a, 502b) and the photographing metadata; measuring an uncertainty value of the neural network model based on a variance value with respect to a probability distribution of resultant data in an inference operation of the neural network model, and measuring uncertainty values of the plurality of images based on an estimated variance value of the resultant data in the inference operation of the neural network model; modifying at least one of the plurality of images based on the uncertainty values of the plurality of images; modifying a labeling policy of the neural network model based on the uncertainty value of the neural network model; generating a 3D model corresponding to the polycystic kidney based on the estimated polycystic kidney region (502a, 502b) and displaying the 3D model on a display; and modifying the estimated polycystic kidney region (502a, 502b) in response to a user input with respect to the 3D model displayed on the display and using the modified polycystic kidney region (502a, 502b) as an input data of the 3D CNN, wherein measuring an uncertainty value of the neural network model comprises performing random prediction based on a sample of the probability distribution, by performing dropout in an inference operation by sampling k thinned neural nets and using an average value of each prediction value, wherein the probability distribution is the probability distribution of resultant data acquired in the inference operation of the neural network model, an average value is acquired as a predicted value, and a variance value is determined as the uncertainty value. acquiring a plurality of captured images of the polycystic kidney with a variety of shapes that are anatomically inconsistent, and photographing metadata and preprocessing the plurality of images to acquire a plurality of image patches (501a, 501b) of a specified size; inputting the plurality of image patches (501a, 501b) into a 3D CNN, Convolutional Neural Network, based neural network model and estimating a polycystic kidney region (502a, 502b) corresponding to each of the plurality of image patches (501a, 501b); measuring a volume of the polycystic kidney by using an area of the estimated polycystic kidney region (502a, 502b) and the photographing metadata; measuring an uncertainty value of the neural network model based on a variance value with respect to a probability distribution of resultant data in an inference operation of the neural network model, and measuring uncertainty values of the plurality of images based on an estimated variance value of the resultant data in the inference operation of the neural network model; modifying at least one of the plurality of images based on the uncertainty values of the plurality of images; modifying a labeling policy of the neural network model based on the uncertainty value of the neural network model; generating a 3D model corresponding to the polycystic kidney based on the estimated polycystic kidney region (502a, 502b) and displaying the 3D model on a display; and modifying the estimated polycystic kidney region (502a, 502b) in response to a user input with respect to the 3D model displayed on the display and using the modified polycystic kidney region (502a, 502b) as an input data of the 3D CNN, wherein measuring an uncertainty value of the neural network model comprises performing random prediction based on a sample of the probability distribution, by performing dropout in an inference operation by sampling k thinned neural nets and using an average value of each prediction value, wherein the probability distribution is the probability distribution of resultant data acquired in the inference operation of the neural network model, an average value is acquired as a predicted value, and a variance value is determined as the uncertainty value.