Accurate pancreas segmentation is highly crucial for diagnosing and treating pancreatic diseases. Although CNN has demonstrated promising outcomes, the performance on unseen data can be significantly compromised by the wide appearance-style variations induced by different imaging factors. Thus, we propose a generalizable pancreas segmentation model based on a meta-learning strategy and latent-space feature flow generation method. Our approach enhances the generalizability by systematically reducing the interference from the cluttered background and appearance-style discrepancies through a coarse-to-fine workflow. Specifically, the integrity-preserving coarse segmentation module is designed to adaptively balance the pancreas coverage and segmentation accuracy with the meta-learning strategy for filtering out background clutter. It also enhances the generalization of the coarse model to reasonably-accurate ROIs thereby promoting the stability of fine segmentation. Subsequently, the appearance-style feature flow generation method is developed to generate a series of progressively-varying style-related intermediate representations between two latent spaces. This feature flow effectively models the distribution variations caused by appearance-style discrepancies, and thus enhances the adaptability of the fine model. Our method achieves superior performance on three pancreas datasets and outperforms state-of-the-art generalization methods. Besides, it can be easily integrated into other workflows, leading to a potential paradigm for enhancing generalization performance.