More robust aircraft maintenance planning can be achieved by accounting for workforce availability uncertainty, thereby reducing the risk of understaffing and costly Aircraft on Ground (AOG) situations. However, traditional forecasting methods in this domain typically rely on point estimates and fail to account for the long-term unpredictability of manpower due to sickness, vacation, or educational leave. This study addresses this gap by developing a probabilistic time series forecasting framework to model daily uncertainty in workforce availability by skill group for horizons up to one year. Drawing on a novel dataset of workforce absence and schedules provided by KLM Engineering \& Maintenance, the study benchmarks a diverse set of architectures, including classical statistical baselines, tree-based methods (LightGBM), and Deep Learning models (DeepAR, NLinear, TSMixer), on their ability to generate well-calibrated predictive distributions. The experimental results demonstrate that the LightGBM architecture, optimised via quantile regression, consistently outperforms complex deep learning and statistical alternatives. Furthermore, it improves point accuracy by approximately 10\% over the current internal forecasting standard at short horizons and 5\% at long horizons, while maintaining experimental calibration above 91\% for its forecasted 95\% confidence intervals. Feature ablation studies reveal distinct temporal dynamics: while long-term, one-year-ahead forecasts are predominantly driven by deterministic calendar and holiday features, short-term accuracy benefits from recent historical absence patterns. A critical operational validation further highlights that while incorporating planned absences improves uncertainty estimates during training, inclusion leads to overconfident predictions due to the addition of significant feature uncertainty. Instead, this study recommends a robust, feature-based probabilistic approach that leverages SHAP values to provide planners with actionable, transparent insights into availability risk.