Medical image segmentation is a key technology in computer aided diagnosis. As a widespread eye disease, glaucoma may cause permanent loss in vision and its screening and diagnosis requires accurate segmentation of optic cup and disc from fundus images. Most traditional computer vision methods segment optic cup and disc with artificial features lead to limited generalization ability. While the end-to-end learning models based on convolutional neural networks focus on optic disc and cup segmentation using automatically detected features, but fail to tackle the lack of labeled samples, thus the segmentation performance is still barely satisfactory. This study proposes an effective two-stage optic disc and cup segmentation method based on semi-supervised conditional generative adversarial nets, namely CDR- GANs. Each stage builds upon three players—A segmentation net, a generator, and a discriminator, where the segmentation net and generator concentrate on learning the conditional distributions between fundus images and their corresponding segmentation maps, and the discriminator distinguishes whether the image-label pairs come from the empirical joint distribution. The extensive experiments show that the proposed method achieves state-of-the-art optic cup and disc segmentation results on ORIGA dataset.