Counterarguments against implementing uncertainty estimation using Bayesian neural networks may include concerns about computational complexity and resource requirements. BNNs typically involve multiple forward passes during inference to compute mean values and standard deviations for each output point, which can be computationally intensive compared to deterministic models. Additionally, training BNNs often requires larger datasets due to the stochastic nature of parameter distributions, potentially increasing data acquisition costs and storage needs. There may also be challenges related to interpretability and explainability since probabilistic outputs from BNNs might be harder to understand for non-experts compared to deterministic models.