  Deep convolutional neural networks have shown outstanding performance in
medical image segmentation tasks. The usual problem when training supervised
deep learning methods is the lack of labeled data which is time-consuming and
costly to obtain. In this paper, we propose a novel uncertainty-guided
semi-supervised learning based on a student-teacher approach for training the
segmentation network using limited labeled samples and a large number of
unlabeled images. First, a teacher segmentation model is trained from the
labeled samples using Bayesian deep learning. The trained model is used to
generate soft segmentation labels and uncertainty maps for the unlabeled set.
The student model is then updated using the softly segmented samples and the
corresponding pixel-wise confidence of the segmentation quality estimated from
the uncertainty of the teacher model using a newly designed loss function.
Experimental results on a retinal layer segmentation task show that the
proposed method improves the segmentation performance in comparison to the
fully supervised approach and is on par with the expert annotator. The proposed
semi-supervised segmentation framework is a key contribution and applicable for
biomedical image segmentation across various imaging modalities where access to
annotated medical images is challenging
