  We propose Uncertainty Augmented Context Attention network (UACANet) for
polyp segmentation which consider a uncertain area of the saliency map. We
construct a modified version of U-Net shape network with additional encoder and
decoder and compute a saliency map in each bottom-up stream prediction module
and propagate to the next prediction module. In each prediction module,
previously predicted saliency map is utilized to compute foreground, background
and uncertain area map and we aggregate the feature map with three area maps
for each representation. Then we compute the relation between each
representation and each pixel in the feature map. We conduct experiments on
five popular polyp segmentation benchmarks, Kvasir, CVC-ClinicDB, ETIS,
CVC-ColonDB and CVC-300, and achieve state-of-the-art performance. Especially,
we achieve 76.6% mean Dice on ETIS dataset which is 13.8% improvement compared
to the previous state-of-the-art method. Source code is publicly available at
https://github.com/plemeri/UACANet
