This paper addresses the problem of an efficient predictive density estimation for the density $q(\|y-θ\|^2)$ of $Y$ based on $X \sim p(\|x-θ\|^2)$ for $y, x, θ\in \mathbb{R}^d$. The chosen criteria are integrated $L_1$ loss given by $L(θ, \hat{q}) \, =\, \int_{\mathbb{R}^d} \big|\hat{q}(y)- q(\|y-θ\|^2) \big| \, dy$, and the associated frequentist risk, for $θ\in Θ$. For absolutely continuous and strictly decreasing $q$, we establish the inevitability of scale expansion improvements $\hat{q}_c(y;X)\,=\, \frac{1}{c^d} q\big(\|y-X\|^2/c^2 \big) $ over the plug-in density $\hat{q}_1$, for a subset of values $c \in (1,c_0)$. The finding is universal with respect to $p,q$, and $d \geq 2$, and extended to loss functions $γ\big(L(θ, \hat{q} ) \big)$ with strictly increasing $γ$. The finding is also extended to include scale expansion improvements of more general plug-in densities $q(\|y-\hatθ(X)\|^2 \big)$, when the parameter space $Θ$ is a compact subset of $\mathbb{R}^d$. Numerical analyses illustrative of the dominance findings are presented and commented upon. As a complement, we demonstrate that the unimodal assumption on $q$ is necessary with a detailed analysis of cases where the distribution of $Y|θ$ is uniformly distributed on a ball centered about $θ$. In such cases, we provide a univariate ($d=1$) example where the best equivariant estimator is a plug-in estimator, and we obtain cases (for $d=1,3$) where the plug-in density $\hat{q}_1$ is optimal among all $\hat{q}_c$.