  The comprehensive integration of machine learning healthcare models within
clinical practice remains suboptimal, notwithstanding the proliferation of
high-performing solutions reported in the literature. A predominant factor
hindering widespread adoption pertains to an insufficiency of evidence
affirming the reliability of the aforementioned models. Recently, uncertainty
quantification methods have been proposed as a potential solution to quantify
the reliability of machine learning models and thus increase the
interpretability and acceptability of the result. In this review, we offer a
comprehensive overview of prevailing methods proposed to quantify uncertainty
inherent in machine learning models developed for various medical image tasks.
Contrary to earlier reviews that exclusively focused on probabilistic methods,
this review also explores non-probabilistic approaches, thereby furnishing a
more holistic survey of research pertaining to uncertainty quantification for
machine learning models. Analysis of medical images with the summary and
discussion on medical applications and the corresponding uncertainty evaluation
protocols are presented, which focus on the specific challenges of uncertainty
in medical image analysis. We also highlight some potential future research
work at the end. Generally, this review aims to allow researchers from both
clinical and technical backgrounds to gain a quick and yet in-depth
understanding of the research in uncertainty quantification for medical image
analysis machine learning models.
