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Currently, the way deep learning models recognize uncertainty remains inconsistent with human perception. In multi-label classification, quantifying uncertainty at the label level presents challenges, as each label may exhibit distinct model confidence levels. Understanding and decomposing label-specific uncertainty is essential for interpreting model behavior and ensuring reliable predictions. We build a hierarchical Bayesian methodology for multi-label classification that leverages a Type II likelihood and Empirical Bayes. Then we estimate and decompose label-wise uncertainties by the bias-variance decomposition. Our approaches offer four main contributions: (1) Type II likelihood maximization is data likelihood centric; (2) it can decompose label-wise uncertainty into the model variance, the model bias and data noise; (3) our uncertainty represented by model bias is intuitively interpretable when combined with observational data; and (4) when applied to out-of-distribution (OOD) detection task, it achieves a 6.88\% lower FPR95 score on NUS-WIDE.