Let $\hatΣ=\frac{1}{n}\sum_{i=1}^n X_i\otimes X_i$ denote the sample covariance operator of centered i.i.d.~observations $X_1,\dots,X_n$ in a real separable Hilbert space, and let $Σ=\mathbb{E}(X_1\otimes X_1)$. The focus of this paper is to understand how well the bootstrap can approximate the distribution of the operator norm error $\sqrt n\|\hatΣ-Σ\|_{\text{op}}$, in settings where the eigenvalues of $Σ$ decay as $λ_j(Σ)\asymp j^{-2β}$ for some fixed parameter $β>1/2$. Our main result shows that the bootstrap can approximate the distribution of $\sqrt n\|\hatΣ-Σ\|_{\text{op}}$ at a rate of order $n^{-\frac{β-1/2}{2β+4+ε}}$ with respect to the Kolmogorov metric, for any fixed $ε>0$. In particular, this shows that the bootstrap can achieve near $n^{-1/2}$ rates in the regime of large $β$ -- which substantially improves on previous near $n^{-1/6}$ rates in the same regime. In addition to obtaining faster rates, our analysis leverages a fundamentally different perspective based on coordinate-free techniques. Moreover, our result holds in greater generality, and we propose a model that is compatible with both elliptical and Marčenko-Pastur models in high-dimensional Euclidean spaces, which may be of independent interest.