## 2 Related Work  
Many previous works focused on uncertainty measurement with DL for segmenting and classifying medical images. The following section reviews prior research related to DL model architecture, training and testing datasets, the dropout rate for measuring uncertainty with MC models, anatomic locations, image types, and ground truth uncertainty. [PERSON] et al. [7] estimate uncertainty for polyps in a colorectal cancer dataset. Three types of architecture named U-Net, SegNet, and FCN-8 were used to segment polyps in the 912 RGB images for 36 patients. The MC dropout was adopted to measure uncertainty with a fixed 50% dropout rate and 10 samples per prediction. Among the neural networks used, FCN-8 achieved the highest IoU and accuracy. [PERSON] et al. [8] used 1115 samples to predict segmentation uncertainty, and a 50% dropout rate was set to assess segmentation uncertainty. U-Net architecture was used with a 5 by 5 filter layer and batch normalization to segment myocardial blood flow in the 478 ASL (Myocardial arterial spin labeling) image dataset. This work also investigated the number of dropout samples on inference time and different hyperparameters selection for the Tversky loss function on the estimation accuracy. [PERSON] et al. [6] also proposed U-Net architecture to estimate uncertainty with the ensemble technique. A total of twelve architectures were used; the first six U-Net architectures were created based on 7, 11, 15, 19, 23, and 27 convolutional layers. The remaining architectures were created using a pixel-wise voting technique of the output of the U-Net architectures. The ensemble models were trained on cardiac disorders datasets with a 50% dropout rate and 20 samplings per prediction. After 240 epochs of training, the ensemble model outperformed all individual architectures. Works like [PERSON] et al. [9], [PERSON] et al. [10], [PERSON] et al. [11] used similar uncertainty measurements for other medical datasets. For example, [PERSON] et al. [10] introduced a U-Net model for segmenting lesions in the oral cancer dataset. The dataset originally contained 492 images, and six types of augmentation, including rotation, zoom, vertical-flipping, horizontal-flipping, brightness adjustment, and gamma correction, were applied to the dataset. Again, a fixed 50% dropout rate with 100 samples per prediction was applied, and the model with MC dropout outperformed the dropout-less U-Net counterpart. [PERSON] et al. [9] introduced an optical coherence tomography dataset for Stargardt disease. Likewise, a 50% dropout rate was used, yet 2, 5, 10, 20, and 30 samples per prediction were used differently to investigate the effect of sampling on the uncertainty segmentation. A multi-class variant of U-Net model was used to segment the eye's vitreous, retina, and choroid-scleral regions. Finally, [PERSON] et al. [11] proposed a U-Net architecture for segmenting cardiac regions for a magnetic resonance imaging (MRI) dataset. This work differs from previously mentioned papers since a 10% dropout rate was used instead of 50% and only five samples were used to estimate uncertainty of a single prediction. At the same time, the U-Net model trained for 1000 epochs (higher than others), achieved better results when compared with other models trained with the same dataset. [PERSON] et al. [12] proposed U-Net architecture for segmenting selected brain parts. Multiple datasets were used in this work: the IXI dataset and part of the MALC dataset were used to train the model, and the remaining samples of the MALC, the ADNI dataset, the CANDI dataset, and the IBSR dataset were used to test the performance of the Bayesian model. The 20% dropout rate and 3, 6, 9, 12, 15, and 18 samples per prediction were used to segment 33 brain structures. [PERSON] et al. [13] proposed a semi-supervised segmentation with MC dropout to segment the structures of the human hip. The authors studied multiple DL architectures trained on both labeled and unlabeled datasets.