In medical image segmentation, single-source domain generalization aims to improve model robustness and performance on unseen target domains using only single-source training data. Single-source domain generalization can mitigate the challenge of limited domain diversity in training, which arises from the impracticality of freely collecting patient data from frontline healthcare institutions due to ethical, legal, and privacy constraints. In this paper, we propose Constrained Learnable Channel-wise Normalization (CLCN), a novel method for single-source domain generalization in medical image segmentation. CLCN comprises two key components: Learnable Channel-wise Normalization (LCN) and Feature Consistency Constraint (FCC). LCN dynamically adjusts feature distributions by learning channel-wise normalization parameters, reducing redundancy and improving the model’s adaptability to diverse and unknown target domains. To address the risk of over-transformation, FCC is introduced to regulate the extent of feature transformations by enforcing consistency between features with and without channel-wise normalization. For evaluation, we conduct experiments on the Prostate dataset across six single-source domain generalization tasks, demonstrating that CLCN outperforms existing methods. Ablation studies further validate the effectiveness of LCN and FCC, showcasing their complementary roles in balancing generalization ability and feature representation. These results highlight the potential of CLCN in advancing domain-adaptive medical image segmentation.