Reliable uncertainty quantification (UQ) is crucial for deploying deep learning models in safety-critical domains. Existing UQ methods often either rely on multi-pass inference, which increases computational cost, or restrict expressiveness by using only final layer embeddings. In this work, we propose a lightweight evidential meta-model that leverages multi-layer feature fusion from a frozen classifier, capturing both low-level textures and high-level semantics to better estimate uncertainty. To further enhance epistemic fidelity, we integrate maximum weight-entropy (Max-WEnt) regularization, which encourages hypothesis diversity without altering the base network or adding test-time overhead. Experiments across seven benchmarks, including medical (BACH, HAM10000, BreakHIS) and natural image datasets (SVHN, Fashion-MNIST, ImageNet-C), demonstrate consistent improvements in AUROC and calibration compared to prior post-hoc UQ methods. Our findings show that combining multi-layer evidential modelling with Max-WEnt provides a robust, efficient, and practical framework for trustworthy AI in high-stakes applications.