We present a single-stage uncertainty-aware approach for jersey number recognition in soccer. Our method employs digit-compositional classifiers that leverage the structural relationships between digits, coupled with a Dirichlet-based uncertainty modeling framework and a tracklet aggregation that combines frame-level predictions using confidence-based filtering. Unlike previous approaches that focus primarily on detecting visible jerseys, we reframe the problem through the lens of uncertainty quantification. This perspective enables more nuanced predictions, particularly in challenging cases of partial visibility or occlusion. Our unified architecture directly processes player crops without requiring explicit jersey detection as in traditional multi-stage pipelines. Through extensive experiments on the SoccerNet and Copa America (CA, ours) datasets, we demonstrate that digit-compositional approaches consistently outperform independent classifiers, while Dirichlet-based uncertainty modeling further improves performance by providing better calibrated confidence estimates across visibility conditions. We achieve high performance on the SoccerNet Challenge benchmark with 85.62% tracklet-level accuracy.