Accurate and robust anomaly detection is critical for microservice system reliability. Recent multimodal approaches have improved detection comprehensiveness by integrating metrics, logs, and traces. However, they often overlook intra-modal uncertainty from noise, ambiguity, or missing data, and inter-modal uncertainty arising from varying predictive capabilities across modalities. Additionally, extensive labeling of multimodal data remains costly. To address these limitations, we propose MUAD, an uncertainty-aware multimodal anomaly detection framework with active learning. MUAD employs a Graph-based Probabilistic Encoder (GPE) to model intra-modal uncertainty through probabilistic representations, and a Confidence-aware Fusion Mechanism (CFM) to dynamically weight modalities based on their prediction confidence. Furthermore, an active learning paradigm iteratively refines the model using high-confidence pseudo-labels and informative samples, maintaining performance under label-deficient conditions. Experiments on three benchmark datasets demonstrate MUAD achieves 98.10% average F1-score, outperforming state-of-the-art methods by up to 7.87%. Results also confirm its robustness under low-quality data and limited labels.