Glaucoma is one of the leading causes of blindness in the world. Optic disc segmentation is an indispensable step for automatic detection of glaucoma with fundus images. In this study, the authors propose an automatic optic disc segmentation approach using adversarial training. The improved ‘U-Net’ is used as the segmentation network to detect optic disc from fundus images, and then the authors add a ‘Patch-level’ adversarial network to enhance higher-order consistency between ground truth and the output from segmentation network, which further boosts the performance of segmentation network. In addition, a new loss function is designed to solve the problem of pixel-level class imbalance in small target region extraction of medical images. All these improvements have effectively increased the segmentation accuracy on hard examples. Authors’ methods achieve Dice coefficient of 0.967 on Drishti-GS dataset and 0.951 on RIM-ONEv3 dataset, which outperform most of the existing methods.