  In recent years, machine learning has witnessed extensive adoption across
various sectors, yet its application in medical image-based disease detection
and diagnosis remains challenging due to distribution shifts in real-world
data. In practical settings, deployed models encounter samples that differ
significantly from the training dataset, especially in the health domain,
leading to potential performance issues. This limitation hinders the
expressiveness and reliability of deep learning models in health applications.
Thus, it becomes crucial to identify methods capable of producing reliable
uncertainty estimation in the context of distribution shifts in the health
sector. In this paper, we explore the feasibility of using cutting-edge
Bayesian and non-Bayesian methods to detect distributionally shifted samples,
aiming to achieve reliable and trustworthy diagnostic predictions in
segmentation task. Specifically, we compare three distinct uncertainty
estimation methods, each designed to capture either unimodal or multimodal
aspects in the posterior distribution. Our findings demonstrate that methods
capable of addressing multimodal characteristics in the posterior distribution,
offer more dependable uncertainty estimates. This research contributes to
enhancing the utility of deep learning in healthcare, making diagnostic
predictions more robust and trustworthy.
