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

Uncertainty-Driven Semantic Segmentation through Human-Machine Collaborative Learning