Glaucoma is a serious eye disease and glaucoma optic disc hemorrhage (GODH) is an important diagnostic indicator for glaucoma. Deep-learning-based medical image segmentation methods for automatic optic cup and disc segmentation have made tremendous progress. However, when it comes to the segmentation of GODH, classical deep learning technologies face two main challenges: the difficulties in distinguishing GODH from the end points or bending points of blood vessels, and the imbalance between the pixel classes of the target area and the background area. In this paper, we proposed a deep learning framework integrating expert knowledge (E-Net) for the segmentation of GODH in fundus images. This E-Net consisted of a primary network for GODH segmentation and two auxiliary networks for extraction of optic disc (OD) and blood vessels. The segmentation probability maps from the two auxiliary networks were used to improve the segmentation accuracy of GODH, via expert knowledge loss functions and attention mechanism. Moreover, we designed a weighted segmentation accuracy loss function to balance the segmentation accuracy of the target and background region, thus fully mining the substantial information in the fundus images. The proposed E-Net was verified on a GODH dataset from Beijing Tongren Hospital. The experiments showed that the proposed E-Net achieved state-of-the-art results on this dataset.