  Effective and reliable screening of patients via Computer-Aided Diagnosis can
play a crucial part in the battle against COVID-19. Most of the existing works
focus on developing sophisticated methods yielding high detection performance,
yet not addressing the issue of predictive uncertainty. In this work, we
introduce uncertainty estimation to detect confusing cases for expert referral
to address the unreliability of state-of-the-art (SOTA) DNNs on COVID-19
detection. To the best of our knowledge, we are the first to address this issue
on the COVID-19 detection problem. In this work, we investigate a number of
SOTA uncertainty estimation methods on publicly available COVID dataset and
present our experimental findings. In collaboration with medical professionals,
we further validate the results to ensure the viability of the best performing
method in clinical practice.
