Rib segmentation is one of the most challenging tasks in the field of medical image segmentation, and also an important tool to assist doctors in diagnosis. However, the challenge of this task is that ribs overlap each other and have low contrast and blurred edges. To address these issues, we propose a novel Disentanglement Enhancement Network called DENet, which disentangles and enhances appearance representations of ribs via the segmentation difficulties and location priors respectively, for robust rib segmentation. In particular, we design a difficulty-guided representation disentanglement module to focus on the most challenging ribs by using the separate decoders for these challenging ribs. To leverage the relations among ribs, we design a location-aware mutual enhancement module, which enables the information exchange and enhancement among different ribs according to the location priors. The experimental results show that mDice of our method is improved by 2% compared to previous methods. Extensive experiments on the dataset demonstrate the effectiveness of our DENet against state-of-the-art medical image segmentation methods.