  Deep co-training has recently been proposed as an effective approach for
image segmentation when annotated data is scarce. In this paper, we improve
existing approaches for semi-supervised segmentation with a self-paced and
self-consistent co-training method. To help distillate information from
unlabeled images, we first design a self-paced learning strategy for
co-training that lets jointly-trained neural networks focus on
easier-to-segment regions first, and then gradually consider harder ones.This
is achieved via an end-to-end differentiable loss inthe form of a generalized
Jensen Shannon Divergence(JSD). Moreover, to encourage predictions from
different networks to be both consistent and confident, we enhance this
generalized JSD loss with an uncertainty regularizer based on entropy. The
robustness of individual models is further improved using a self-ensembling
loss that enforces their prediction to be consistent across different training
iterations. We demonstrate the potential of our method on three challenging
image segmentation problems with different image modalities, using small
fraction of labeled data. Results show clear advantages in terms of performance
compared to the standard co-training baselines and recently proposed
state-of-the-art approaches for semi-supervised segmentation
