## 2 Related Work
In the case of MC dropout, both 10% and 50% with 50 samples per prediction were evaluated. Both datasets are multi-class segmentation datasets, and after 50 epochs of training, the ensemble model achieved superior segmentation accuracy. Yet, uncertainty with 10% MC achieved better results compared to 50% MC dropout. Lastly, [PERSON] et al. [18] adapted the uncertainty concept for classifying breast cancer tissues using BNN. The dataset contained four classes based on the HER2 factor categorized as 0, 1+, 2+, and 3+. One of the experiments tested a dropout rate of 20%, 50%, and 80% during the testing process. Using 5-fold cross-validation after 200 epochs, the model with a 20% dropout rate achieved the highest result compared to other dropout rates. Finally, [PERSON] et al. [1] proposed the Bayesian approach and integrated it with the original structure of the U-Net architecture. The authors claimed that the model makes various predictions with high uncertainty for hard inputs. Conversely, the model remains consistent when input is easy during a training process. The model was trained and evaluated based on two public MRI cardiac datasets. The proposed model slightly outperforms MC dropout and achieves higher efficiency compared to the ensemble model. [PERSON] et al. [5] segmented two medical datasets using U-Net architecture. To measure uncertainty, the hierarchical probabilistic method creates the conditional distribution for input samples. The model generates low-resolution segmentation samples and then improves the distribution in higher resolutions using multiple latent variables. The model uses LIDC-IDRI 3D CT images and 2D MRI for the prostate, both annotated by four radiologists. The model trained with single and multiple annotations separately achieved superior results compared to deterministic and probabilistic U-Net. [PERSON] et al. [19] combined U-Net architecture with a conditional variational autoencoder (CVAE) to assess uncertainty of segmentation. The CVAE part creates low-dimensional latent space, and then a random sample from this distribution was provided to the U-Net part to predict probabilistic segmentation. A medical and non-medical dataset were used to train and evaluate the model performance. The medical dataset is the LIDC-IDRI, but only 2D sections were used. We tried to reimplement the last two mentioned models based on their public GitHub repository and compare their result with our findings; however, we failed since the code was not maintained for a long time and there was a package inconsistency preventing us from running the code. The previous works mainly focused on specific elements of uncertainty measurement with DL by applying uncertainty architecture for particular datasets or using specific hyperparameters. We believe measuring uncertainty with DL should be covered in more detail in the following aspects:  
* Segmenting (or classifying) images or lesions differ from expert to expert; individual assertions are usually biased and insufficient for robust labeling. Therefore, adding uncertainty to ground truth images is essential. * A robust model needs to be selected based on experiments with different image modalities, different anatomic locations, and multiple task types (multi-class segmentation, binary segmentation). * The impact of dropout rate on uncertainty estimation in MC dropout architectures remains unclear. Some studies achieved the best results with low dropout rates, while others found success with high dropout rates. Therefore, it is essential to assess the impact of dropout rate in a fair and unbiased setting across various image types and with a sufficient training dataset. To fulfill the mentioned gaps, we separately trained the ensemble, the regression, and MC models with 20%, 40%, 60%, and 80% dropout rates. Each model was separately trained with four public datasets labeled by multiple experts. Finally, multiple metrics were used to measure and evaluate the performance of uncertainty models. Table 1 shows the comparison between our and existing literature.