Safety monitoring is essential for Cyber-Physical Systems (CPSs) such as Unmanned Aerial Vehicles (UAVs). However, unsafe events are rare in CPS operations, creating class imbalance that degrades data-driven safety predictors. Behavioral uncertainty, reflected in erratic control signals or rapid heading changes, is often correlated with safety outcomes: uncertain behaviors occur more often during unsafe operation. Yet safety monitoring rarely exploits it. We propose U-Balance, a supervised approach that leverages behavioral uncertainty to rebalance datasets before training a safety predictor. U-Balance first trains a GatedMLP-based uncertainty predictor that represents telemetry windows using distributional kinematic features and outputs an uncertainty score. Its uncertainty-guided label rebalancing (uLNR) mechanism probabilistically relabels $\textit{safe}$ windows with unusually high uncertainty as $\textit{unsafe}$, enriching the minority class without synthesizing data. A safety predictor is then trained on the rebalanced dataset. We evaluate U-Balance on a UAV benchmark and ForEnt, a quadruped-robot entrapment dataset. On UAV, U-Balance achieves F1/AUROC/AUPRC of $0.801/0.972/0.776$, outperforming the strongest safety-prediction baselines by 14.3/2.2/6.1 percentage points ($\textit{pp}$), respectively. While the strongest baseline varies across metrics (MLP for F1 and AUPRC; CNN for AUROC), U-Balance leads on all three. On ForEnt, it achieves $0.522/0.806/0.323$, outperforming the strongest baselines by 4.4/9.2/1.4 $\textit{pp}$. Using the same Bi-LSTM safety predictor, uLNR outperforms the strongest alternative rebalancing methods by 17.0/4.1/8.2 $\textit{pp}$ on UAV and 2.3/6.5/1.9 on ForEnt. uLNR also outperforms Early and Late Fusion on both datasets. To our knowledge, this work is the first to use behavioral uncertainty for dataset rebalancing in CPS safety monitoring.

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