The deployment of convolutional neural network (CNN) in real-time and resource-constrained applications poses critical challenges due to their computational demands and the need for reliable decision making. In this article, we combine adaptive inference via early exits (EE) with selective prediction (SP) to address these challenges. Early exits allow confident predictions at intermediate layers, while SP introduces uncertainty estimation modules, enabling the system to abstain from low-confidence decisions or continue inference through deeper layers. This combined design lowers the risk of overconfident but erroneous predictions and improves the tradeoff between performance and accuracy. As a case study, we implement and evaluate our approach on a real-time traffic sign detection task, processing the input of an RGB camera in the forward direction. In this article, we demonstrate improved performance compared to baseline models. Compared to SP-only (SP) and early exiting (EE)-only (Early Exit) baselines, our hybrid model achieves low inference depth (1.20), leading to reduced computational demand and latency. Despite this efficiency, the model maintains a high prediction accuracy (90.3%) and a low abstention rate (1.6%), ensuring fast and reliable decision making suitable for time-critical embedded applications. This demonstrates an effective tradeoff between effective computation and predictive reliability.