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Speech anti-spoofing systems often degrade in out-of-domain (OOD) settings due to varied unknown spoofing attacks. Domain generalization (DG) methods address this by improving model robustness across diverse domains. Style augmentation is a DG approach that synthesizes new features by modeling domain-shift uncertainty using feature statistics learned during training. However, previous style augmentation methods rely on a single batch-level uncertainty by assuming a unimodal style distribution, which may not hold in mixed domain batches. To address this issue, we propose Gaussian mixture model (GMM) based aleatoric style uncertainty (ASU) to model within-component variability. In addition, we proposed an online EM-like update for the GMM in an end-to-end way. Extensive experiments on anti-spoofing and spoofing-aware speaker verification (SASV) show that ASU significantly outperforms existing methods and surpasses state-of-the-art systems. Code is available at GitHub.