Single-source domain generalization (SDG) is used to improve model’s performance on unseen target domains by utilizing data from one source domain, with a primary emphasis on alleviating the impact of domain shifts. In the context of retinal vessel segmentation, domain shifts often arise due to variations in datasets composition, such as discrepancies in disease prevalence and imaging noise levels. Despite their significance, the underlying mechanisms through which these shifts impact model performance remain insufficiently explored. In this paper, we hypothesize that dataset variations are reflected in the distributional differences of frequency-domain features, which can cause models to overfit to specific patterns within the source dataset. To address the problem, this paper proposes a novel SDG method, denoted as Band Importance Aware Random Frequency Filter based Single-source Domain Generalization (BIRF-SDG). This framework incorporates a band scoring mechanism designed to identify and preserve frequency bands that are critical for segmentation tasks, thereby preventing the loss of essential information in subsequent processes. Furthermore, we propose a random band filtering strategy as a data augmentation technique to improve the model's generalization across various domains. Extensive comparative experiments and ablation analyses on cross-domain retinal image datasets confirm that our method attains state-of-the-art performance, effectively addressing the challenges associated with domain shift in retinal vessel segmentation.