  As a powerful way of realizing semi-supervised segmentation, the cross
supervision method learns cross consistency based on independent ensemble
models using abundant unlabeled images. However, the wrong pseudo labeling
information generated by cross supervision would confuse the training process
and negatively affect the effectiveness of the segmentation model. Besides, the
training process of ensemble models in such methods also multiplies the cost of
computation resources and decreases the training efficiency. To solve these
problems, we propose a novel cross supervision method, namely
uncertainty-guided self cross supervision (USCS). In addition to ensemble
models, we first design a multi-input multi-output (MIMO) segmentation model
which can generate multiple outputs with shared model and consequently impose
consistency over the outputs, saving the cost on parameters and calculations.
On the other hand, we employ uncertainty as guided information to encourage the
model to focus on the high confident regions of pseudo labels and mitigate the
effects of wrong pseudo labeling in self cross supervision, improving the
performance of the segmentation model. Extensive experiments show that our
method achieves state-of-the-art performance while saving 40.5% and 49.1% cost
on parameters and calculations.
