Multi-organ segmentation in medical images is a widely researched task and can save much manual efforts of clinicians in daily routines. Automating the organ segmentation process using deep learning (DL) is a promising solution and state-of-the-art segmentation models, such as nnUNet, are achieving promising accuracy. However, overfitting is still a critical issue for DL-based models due to the limited size of segmentation datasets especially in the medical domain. In this work, a novel data augmentation (DA) strategy is proposed to improve the over-fitting problem of multi-organ segmentation datasets, namely AnatoMix. Different from basic DA strategies based on image transformation on a single image, AnatoMix manipulates multiple images in the segmentation dataset and generates new data by mixing multiple images while maintaining human anatomy as realistic as possible. AnatoMix takes the size and location of each organ into consideration, and the corresponding segmentation ground truth is automatically obtained. The initial experiments have been done to extend the publicly available CT-ORG dataset with AnatoMix generating 1,545 new volumes from the 28 original ones. The extended dataset is then evaluated by training a U-Net on the original and the augmented dataset and tested on the same test data. In our experiments the extended dataset leads to mean dice of 76.1, compared with 74.8 on the original dataset. This shows AnatoMix can effectively improve the generalizability of a limited segmentation dataset.