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Deep learning (DL) methods have demonstrated superior performance in medical image segmentation tasks. However, selecting a loss function that conforms to the data characteristics is critical for optimal performance. Further, the direct use of traditional DL models does not provide a measure of uncertainty in predictions. Even high-quality automated predictions for medical diagnostic applications demand uncertainty quantification to gain user trust. In this study, we aim to investigate thedoi:10.3390/biomedicines10061323 pmid:35740345 pmcid:PMC9220007 fatcat:tv3xewlrsrgbdeib2q6mguhoxq