Federated learning (FL), as a distributed learning paradigm, allows multiple medical institutions to collaborate on learning without the need to centralize all client data. However, existing methods pay little attention to more challenging medical image semantic segmentation tasks, especially in the scenario of the imbalanced dataset in federated few-shot learning (FSL). In this paper, we propose a subnetwork-based federated few-shot organ image segmentation method. Firstly, individual clients train using local training samples and then upload local model gradients to the server. The server utilizes their respective local model gradients to update the subnetwork maintained on the server and generate aggregation weights for forming personalized model parameters. Through this method, we can learn the similarities between different clients to address data heterogeneity issues. In addition, to enhance the communication efficiency between clients and the server, we have also designed a personalized layer aggregation strategy, which only transmits partial layer model parameters during the communication process to improve communication efficiency. Finally, we conducted experiments on abdomen magnetic resonance imaging (ABD-MRI) and abdomen computed tomography (ABD-CT) datasets to demonstrate the effectiveness of our method.