  Deep learning models (DLMs) can achieve state of the art performance in
medical image segmentation and classification tasks. However, DLMs that do not
provide feedback for their predictions such as Dice coefficients (Dice) have
limited deployment potential in real world clinical settings. Uncertainty
estimates can increase the trust of these automated systems by identifying
predictions that need further review but remain computationally prohibitive to
deploy. In this study, we use a DLM with randomly initialized weights and Monte
Carlo dropout (MCD) to segment tumors from microscopic Hematoxylin and Eosin
(H&E) dye stained prostate core biopsy RGB images. We devise a novel approach
that uses multiple clinical region based uncertainties from a single image
(instead of the entire image) to predict Dice of the DLM model output by linear
models. Image level uncertainty maps were generated and showed correspondence
between imperfect model segmentation and high levels of uncertainty associated
with specific prostate tissue regions with or without tumors. Results from this
study suggest that linear models can learn coefficients of uncertainty
quantified deep learning and correlations ((Spearman's correlation (p<0.05)) to
predict Dice scores of specific regions of medical images.
