Real-time planning among many uncertain, dynamic obstacles is challenging because predicting every agent with high fidelity is both unnecessary and computationally expensive. We present Heterogeneous Predictor-based Risk-Aware Planning (H-PRAP), a framework that allocates prediction effort to where it matters. H-PRAP introduces the Probability-based Collision Risk Index (P-CRI), a closed-form, horizon-level collision index obtained by calibrating a Gaussian surrogate with conformal prediction. P-CRI drives a router that assigns high-risk obstacles to accurate but expensive predictors and low-risk obstacles to lightweight predictors, while preserving distribution-free coverage across heterogeneous predictors through conformal prediction. The selected predictions and their conformal radii are embedded in a chance-constrained model predictive control (MPC) problem, yielding receding-horizon policies with explicit safety margins. We analyze the safety-efficiency trade-off under prediction compute budget: more portion of low-fidelity predictions reduce residual risk from dropped obstacles, but in the same time induces larger conformal radii and degrades trajectory efficiency and shrinks MPC feasibility. Extensive numerical simulations in dense, uncertain environments validate that H-PRAP attains best balance between trajectory success rate (i.e., no collisions) and the time to reach the goal (i.e., trajectory efficiency) compared to single prediction architectures.