  Deep learning-based approaches achieve state-of-the-art performance in the
majority of image segmentation benchmarks. However, training of such models
requires a sizable amount of manual annotations. In order to reduce this
effort, we propose a method based on conditional Generative Adversarial Network
(cGAN), which addresses segmentation in a semi-supervised setup and in a
human-in-the-loop fashion. More specifically, we use the discriminator to
identify unreliable slices for which expert annotation is required and use the
generator in the GAN to synthesize segmentations on unlabeled data for which
the model is confident. The quantitative results on a conventional standard
benchmark show that our method is comparable with the state-of-the-art fully
supervised methods in slice-level evaluation requiring far less annotated data.
