Existing training methods for medical image foundation models primarily focus on tasks such as image restoration, overlooking the potential of harnessing the inherent anatomical knowledge of the human body. The discrepancy between the training tasks of foundation models and downstream tasks often necessitates model fine-tuning for each specific application. An insufficient scale of the downstream training set can lead to catastrophic forgetting of the foundational model. To address these issues, we propose a novel unsupervised training method for medical image foundation models. Our approach incorporates an anatomical embedding task, enabling the model to generate anatomically related embeddings for each voxel. To expedite the training and accommodate large-scale models, we employ the strategy of momentum contrast learning, which is further enhanced to adapt to the task of anatomical embedding. To improve the model's performance for specific targets, we introduce the region contrastive loss, utilizing a small set of segmentation labels (e.g., five samples) to identify the focused regions during training. In our experiments, we pre-train the foundation model using a dataset of 4000 unlabeled abdominal CT scans with the downstream task being the few-shot learning of 13 abdominal organ segmentation. The results showed significant improvements in the downstream segmentation task, particularly in the scenarios with limited segmentation annotations, compared to methods without pre-training and similar foundation models. The trained models and the downstream training code have been open sourced at https://github.com/DlutMedimgGroup/Anatomy-Embedding-Foundation-Model .