NeRF On-the-go is a novel approach that enables the robust synthesis of novel views in complex, real-world scenes from casually captured image sequences. The method leverages predicted uncertainty maps to effectively remove dynamic elements (distractors) such as moving objects, shadows, and lighting changes, which are common in dynamic scenes. Unlike existing methods like NeRF-W and RobustNeRF, which struggle with imperfect results, NeRF On-the-go achieves high-fidelity novel view synthesis by efficiently eliminating distractors and achieving faster convergence. The method uses DINOv2 features for robust and spatial-temporal consistent feature extraction, a small MLP to predict per-sample pixel uncertainty, and a structural similarity loss to enhance uncertainty optimization. The estimated uncertainty is incorporated into the NeRF's image reconstruction objective using a decoupled training strategy, significantly improving distractor elimination, especially in high occlusion scenes. Comprehensive experiments on various scenes demonstrate significant improvements over state-of-the-art techniques, making NeRF On-the-go a powerful tool for enhancing NeRF training in dynamic real-world settings.NeRF On-the-go is a novel approach that enables the robust synthesis of novel views in complex, real-world scenes from casually captured image sequences. The method leverages predicted uncertainty maps to effectively remove dynamic elements (distractors) such as moving objects, shadows, and lighting changes, which are common in dynamic scenes. Unlike existing methods like NeRF-W and RobustNeRF, which struggle with imperfect results, NeRF On-the-go achieves high-fidelity novel view synthesis by efficiently eliminating distractors and achieving faster convergence. The method uses DINOv2 features for robust and spatial-temporal consistent feature extraction, a small MLP to predict per-sample pixel uncertainty, and a structural similarity loss to enhance uncertainty optimization. The estimated uncertainty is incorporated into the NeRF's image reconstruction objective using a decoupled training strategy, significantly improving distractor elimination, especially in high occlusion scenes. Comprehensive experiments on various scenes demonstrate significant improvements over state-of-the-art techniques, making NeRF On-the-go a powerful tool for enhancing NeRF training in dynamic real-world settings.