  Medical image segmentation has significantly benefitted thanks to deep
learning architectures. Furthermore, semi-supervised learning (SSL) has
recently been a growing trend for improving a model's overall performance by
leveraging abundant unlabeled data. Moreover, learning multiple tasks within
the same model further improves model generalizability. To generate smoother
and accurate segmentation masks from 3D cardiac MR images, we present a
Multi-task Cross-task learning consistency approach to enforce the correlation
between the pixel-level (segmentation) and the geometric-level (distance map)
tasks. Our extensive experimentation with varied quantities of labeled data in
the training sets justifies the effectiveness of our model for the segmentation
of the left atrial cavity from Gadolinium-enhanced magnetic resonance (GE-MR)
images. With the incorporation of uncertainty estimates to detect failures in
the segmentation masks generated by CNNs, our study further showcases the
potential of our model to flag low-quality segmentation from a given model.
