A method for image segmentation by using at least one adaptive loss weight map, comprising steps of: (a) a learning device, when at least one input image is acquired, (i) instructing an encoding layer of a CNN to generate at least one feature map by applying one or more convolution operations to the input image, and (ii) instructing a decoding layer of the CNN to generate each of segmentation scores corresponding to each of pixels of the input image by applying one or more deconvolution operations to the feature map; (b) the learning device instructing a softmax layer of the CNN to generate each of softmax scores corresponding to each of the pixels of the input image by applying softmax operations to each of the segmentation scores; (c) the learning device instructing a loss weight layer of the CNN (i) to generate each of predicted error values corresponding to each of the pixels of the input image by referring to the softmax scores and their corresponding ground truth, GT, values, and (ii) to generate each of loss weight values corresponding to each of the pixels of the input image by applying loss weight operations to each of the predicted error values, wherein the loss weight operations allow differences among each of the loss weight values to be larger than those among each of the predicted error values; and (d) the learning device (i) instructing a softmax loss layer of the CNN to generate each of initial softmax loss values corresponding to each of the pixels of the input image by referring to each of the softmax scores and their corresponding GT values, and to generate each of adjusted softmax loss values corresponding to each of the pixels of the input image by referring to each of the initial softmax loss values and each of the loss weight values, and (ii) learning parameters of the CNN by backpropagating the adjusted softmax loss values, characterized in that at step (c), the loss weight operations follow the formula below, loss weight value for i − th pixel = α i × E i + β i wherein α i is a scaling parameter corresponding to an i-th pixel among the pixels, E i is the predicted error value corresponding to the i-th pixel among the pixels, β i is a bias parameter corresponding to an i-th pixel among the pixels, i is an integer from 1 to s, and s is the number of the pixels included in the input image. (a) a learning device, when at least one input image is acquired, (i) instructing an encoding layer of a CNN to generate at least one feature map by applying one or more convolution operations to the input image, and (ii) instructing a decoding layer of the CNN to generate each of segmentation scores corresponding to each of pixels of the input image by applying one or more deconvolution operations to the feature map; (b) the learning device instructing a softmax layer of the CNN to generate each of softmax scores corresponding to each of the pixels of the input image by applying softmax operations to each of the segmentation scores; (c) the learning device instructing a loss weight layer of the CNN (i) to generate each of predicted error values corresponding to each of the pixels of the input image by referring to the softmax scores and their corresponding ground truth, GT, values, and (ii) to generate each of loss weight values corresponding to each of the pixels of the input image by applying loss weight operations to each of the predicted error values, wherein the loss weight operations allow differences among each of the loss weight values to be larger than those among each of the predicted error values; and (d) the learning device (i) instructing a softmax loss layer of the CNN to generate each of initial softmax loss values corresponding to each of the pixels of the input image by referring to each of the softmax scores and their corresponding GT values, and to generate each of adjusted softmax loss values corresponding to each of the pixels of the input image by referring to each of the initial softmax loss values and each of the loss weight values, and (ii) learning parameters of the CNN by backpropagating the adjusted softmax loss values,